Abstract #1292

An Evaluation of the Diagnostic Quality of Machine Learning Approaches for PET Attenuation Correction in Neuroimaging: A Meta-Analysis

Confidence Raymond^1,2, Jurkiewicz Michael ^1,2, Akin Orunmuyi³, Dada Oluwaseun Michael ⁴, Claes Nøhr Ladefoged⁵, Jarmo Teuho⁶, and Udunna Anazodo^1,2

¹Medical Biophysics, Western University Ontario, London, ON, Canada, ²Lawson Health Research Institute, LONDON, ON, Canada, ³Anaesthesia, College of Medicine, Ibadan, Nigeria, ⁴Physics, Federal University of Technology, Minna, Nigeria, ⁵Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Denmark, ⁶Turku PET Centre, Turku University, Turku, Finland

Synopsis

The last decade has seen an increase in the application of machine learning (ML) methods to PET/MRI attenuation correction (AC). This systematic review provides a head-to-head comparison between state-of-the-art ML methods and clinical standards for AC to determine the clinical feasibility of ML approaches PET AC. We extracted numerical values for image quality, tissue classification, regional and global diagnostic performance. The pooled mean relative error for global performance was 0.87 ± 1.3%, the quality of evidence for all outcomes ranged from moderate to very low. Our findings suggest that ML-AC performance is within acceptable limits for clinical PET/MR neuroimaging.

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